knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scRNAseq_2.2.0 limma_3.44.3
## [3] tidyr_1.1.1 dplyr_1.0.2
## [5] SingleR_1.2.4 MAST_1.14.0
## [7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2
## [9] DelayedArray_0.14.1 matrixStats_0.56.0
## [11] Biobase_2.48.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [17] data.table_1.13.0 ggplot2_3.3.2
## [19] Seurat_3.2.0
##
## loaded via a namespace (and not attached):
## [1] AnnotationHub_2.20.1 BiocFileCache_1.12.1
## [3] plyr_1.8.6 igraph_1.2.5
## [5] lazyeval_0.2.2 splines_4.0.2
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] digest_0.6.25 htmltools_0.5.0
## [11] magrittr_1.5 memoise_1.1.0
## [13] tensor_1.5 cluster_2.1.0
## [15] ROCR_1.0-11 globals_0.12.5
## [17] colorspace_1.4-1 blob_1.2.1
## [19] rappdirs_0.3.1 ggrepel_0.8.2
## [21] xfun_0.16 crayon_1.3.4
## [23] RCurl_1.98-1.2 jsonlite_1.7.0
## [25] spatstat_1.64-1 spatstat.data_1.4-3
## [27] survival_3.2-3 zoo_1.8-8
## [29] ape_5.4-1 glue_1.4.1
## [31] polyclip_1.10-0 gtable_0.3.0
## [33] zlibbioc_1.34.0 XVector_0.28.0
## [35] leiden_0.3.3 BiocSingular_1.4.0
## [37] future.apply_1.6.0 abind_1.4-5
## [39] scales_1.1.1 DBI_1.1.0
## [41] miniUI_0.1.1.1 Rcpp_1.0.5
## [43] viridisLite_0.3.0 xtable_1.8-4
## [45] reticulate_1.16 bit_4.0.4
## [47] rsvd_1.0.3 htmlwidgets_1.5.1
## [49] httr_1.4.2 RColorBrewer_1.1-2
## [51] ellipsis_0.3.1 ica_1.0-2
## [53] pkgconfig_2.0.3 uwot_0.1.8
## [55] dbplyr_1.4.4 deldir_0.1-28
## [57] tidyselect_1.1.0 rlang_0.4.7
## [59] reshape2_1.4.4 later_1.1.0.1
## [61] AnnotationDbi_1.50.3 munsell_0.5.0
## [63] BiocVersion_3.11.1 tools_4.0.2
## [65] generics_0.0.2 RSQLite_2.2.0
## [67] ExperimentHub_1.14.1 ggridges_0.5.2
## [69] evaluate_0.14 stringr_1.4.0
## [71] fastmap_1.0.1 yaml_2.2.1
## [73] goftest_1.2-2 knitr_1.29
## [75] bit64_4.0.2 fitdistrplus_1.1-1
## [77] purrr_0.3.4 RANN_2.6.1
## [79] pbapply_1.4-3 future_1.18.0
## [81] nlme_3.1-148 mime_0.9
## [83] compiler_4.0.2 plotly_4.9.2.1
## [85] curl_4.3 png_0.1-7
## [87] interactiveDisplayBase_1.26.3 spatstat.utils_1.17-0
## [89] tibble_3.0.3 stringi_1.4.6
## [91] lattice_0.20-41 Matrix_1.2-18
## [93] vctrs_0.3.2 pillar_1.4.6
## [95] lifecycle_0.2.0 BiocManager_1.30.10
## [97] lmtest_0.9-37 RcppAnnoy_0.0.16
## [99] BiocNeighbors_1.6.0 cowplot_1.0.0
## [101] bitops_1.0-6 irlba_2.3.3
## [103] httpuv_1.5.4 patchwork_1.0.1
## [105] R6_2.4.1 promises_1.1.1
## [107] KernSmooth_2.23-17 gridExtra_2.3
## [109] codetools_0.2-16 MASS_7.3-52
## [111] assertthat_0.2.1 withr_2.2.0
## [113] sctransform_0.2.1 GenomeInfoDbData_1.2.3
## [115] mgcv_1.8-31 grid_4.0.2
## [117] rpart_4.1-15 rmarkdown_2.3
## [119] DelayedMatrixStats_1.10.1 Rtsne_0.15
## [121] shiny_1.5.0
## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.
Going to analyze the MEP cluster, which is of greatest interest to us.
To see if this MEP cluster is playing a role in fibrogenesis.
Looking to find what genes distinguish this MEP cluster from all other clusters in the analysis.
This was also done for down-regulated markers, but it is not very informative as there are few genes that are widely expressed in all other clusters excluding MEP cluster.
Going to look at a few different subclusterings:
Note: the original plan was to do this in Seurat, but this is where Monocle may be more useful to look at trajectories.
## [1] "Cells in Subset 1"
##
## Gran-1 Gran-2 ?GMP B cell-1 Gran-3
## 0 0 0 0 0
## Monocyte ?MEP/Mast ?CMP/Neutro Macrophage B cell-2
## 0 592 0 0 0
## Erythrocyte T cell Megakaryocyte B cell-3 B cell-4
## 312 0 178 0 0
##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
## Gran-1 0 0 0 0 0 0
## Gran-2 0 0 0 0 0 0
## ?GMP 0 0 0 0 0 0
## B cell-1 0 0 0 0 0 0
## Gran-3 0 0 0 0 0 0
## Monocyte 0 0 0 0 0 0
## ?MEP/Mast 12 500 10 11 51 8
## ?CMP/Neutro 0 0 0 0 0 0
## Macrophage 0 0 0 0 0 0
## B cell-2 0 0 0 0 0 0
## Erythrocyte 65 26 29 76 110 6
## T cell 0 0 0 0 0 0
## Megakaryocyte 8 25 121 6 8 10
## B cell-3 0 0 0 0 0 0
## B cell-4 0 0 0 0 0 0
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## [1] "Cells in Subset 4 (MEPs)"
##
## Gran-1 Gran-2 ?GMP B cell-1 Gran-3
## 0 0 0 0 0
## Monocyte ?MEP/Mast ?CMP/Neutro Macrophage B cell-2
## 0 592 0 0 0
## Erythrocyte T cell Megakaryocyte B cell-3 B cell-4
## 0 0 0 0 0
##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
## Gran-1 0 0 0 0 0 0
## Gran-2 0 0 0 0 0 0
## ?GMP 0 0 0 0 0 0
## B cell-1 0 0 0 0 0 0
## Gran-3 0 0 0 0 0 0
## Monocyte 0 0 0 0 0 0
## ?MEP/Mast 12 500 10 11 51 8
## ?CMP/Neutro 0 0 0 0 0 0
## Macrophage 0 0 0 0 0 0
## B cell-2 0 0 0 0 0 0
## Erythrocyte 0 0 0 0 0 0
## T cell 0 0 0 0 0 0
## Megakaryocyte 0 0 0 0 0 0
## B cell-3 0 0 0 0 0 0
## B cell-4 0 0 0 0 0 0
##
## enrMigr1 enrMpl enrNbeal_cntrl Migr1 Mpl Nbeal_cntrl
## 0 6 166 2 2 9 0
## 1 0 165 0 0 14 0
## 2 0 75 0 2 5 0
## 3 4 32 8 6 16 8
## 4 0 55 0 0 3 0
## 5 2 7 0 1 4 0
##
## Control Migr1 Mpl
## 18 23 551
## [1] 9 5
## [1] 113 5
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Slpi 2.923376e-23 2.864115 0.964 0.739 5.294527e-19
## Akr1c18 2.491825e-15 2.638171 0.835 0.043 4.512945e-11
## Ccl4 6.282744e-12 2.556199 0.911 0.391 1.137868e-07
## Furin 1.340648e-30 1.838023 0.989 0.783 2.428047e-26
## Cfp 5.628861e-16 1.594472 0.940 0.348 1.019443e-11
## Ccl6 4.286424e-21 1.450810 0.978 0.739 7.763142e-17
## [1] 185 5
## p_val avg_logFC pct.1 pct.2 p_val_adj
## Slpi 7.031209e-39 2.962248 0.964 0.707 1.273422e-34
## Ccl4 9.744238e-24 2.864216 0.911 0.317 1.764779e-19
## Akr1c18 2.707028e-27 2.753610 0.835 0.024 4.902698e-23
## Furin 4.255144e-54 2.032867 0.989 0.659 7.706491e-50
## Ccl6 3.703546e-53 1.983642 0.978 0.634 6.707492e-49
## S100a6 1.539080e-71 1.865602 0.998 0.561 2.787428e-67
Looking at the GO terms that are most associated with up- and down-regulated genes from Mpl compared to Nbeal and Migr1. These will be located in a supplementary excel file. See MEP.mpl.vs.migr1ANDnbeal.xlsx